Google Scholar Profile

  • Kumar, S.*, Sumers, T. R.*, Yamakoshi, T., Goldstein, A., Hasson, U., Norman, K. A., Griffiths, T.L., Hawkins, R.D., Nastase, S. A. (2022). Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model. bioRxiv. (under review)

  • Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R. D., Daw, N.D., Cohen, J.D., Narasimhan, K., & Griffiths, T. L. (2022). Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS) 2022. arXiv [Outstanding Paper Award, Top 13/10K+ submissions]

  • Kumar, S., Dasgupta, I., Marjieh, R., Daw, N. D., Cohen, J. D., & Griffiths, T. L. (2022). Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. arXiv (under review)

  • Kumar, S., Dasgupta, I., Cohen, J. D., Daw, N. D., & Griffiths, T. L. (2021). Meta-Learning of Structured Task Distributions in Humans and Machines. In Proceedings of the 9th International Conference on Learning Representations (ICLR) 2021. arXiv

  • Kumar, S., Ellis, C.T., O'Connell, T.P., Chun, M.M., Turk-Browne, N.B. (2020) Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain. PLoS Computational Biology, 16(12) e1008457.

  • Kumar, S., Yoo, K., Rosenberg, M. D., Scheinost, D., Constable, R. T., Zhang, S., ... & Chun, M. M. (2019). An information network flow approach for measuring functional connectivity and predicting behavior. Brain and behavior, 9(8), e01346.